A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities

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Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. In summary, BiomedParse is an all-in-one tool for biomedical image analysis on all major image modalities, paving the path for efficient and accurate image-based biomedical discovery. 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Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. 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data-track-component="article body"> <article lang="en"> <div class="c-article-header"> <header> <ul class="c-article-identifiers" data-test="article-identifier"> <li class="c-article-identifiers__item" data-test="article-category">Article</li> <li class="c-article-identifiers__item">Published: <time datetime="2024-11-18">18 November 2024</time></li> </ul> <h1 class="c-article-title" data-test="article-title" data-article-title="">A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities</h1> <ul class="c-article-author-list c-article-author-list--short" data-test="authors-list" data-component-authors-activator="authors-list"><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Theodore-Zhao-Aff1" data-author-popup="auth-Theodore-Zhao-Aff1" data-author-search="Zhao, Theodore">Theodore Zhao</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup><sup class="u-js-hide"> <a href="#na1">na1</a></sup>, </li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Yu-Gu-Aff1" data-author-popup="auth-Yu-Gu-Aff1" data-author-search="Gu, Yu">Yu Gu</a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0002-1704-1744"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0002-1704-1744</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup><sup class="u-js-hide"> <a href="#na1">na1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Jianwei-Yang-Aff1" data-author-popup="auth-Jianwei-Yang-Aff1" data-author-search="Yang, Jianwei">Jianwei Yang</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Naoto-Usuyama-Aff1" data-author-popup="auth-Naoto-Usuyama-Aff1" data-author-search="Usuyama, Naoto">Naoto Usuyama</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Ho_Hin-Lee-Aff1" data-author-popup="auth-Ho_Hin-Lee-Aff1" data-author-search="Lee, Ho Hin">Ho Hin Lee</a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0002-7378-2379"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0002-7378-2379</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Sid-Kiblawi-Aff1" data-author-popup="auth-Sid-Kiblawi-Aff1" data-author-search="Kiblawi, Sid">Sid Kiblawi</a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0001-6183-3354"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0001-6183-3354</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Tristan-Naumann-Aff1" data-author-popup="auth-Tristan-Naumann-Aff1" data-author-search="Naumann, Tristan">Tristan Naumann</a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0003-2150-1747"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0003-2150-1747</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Jianfeng-Gao-Aff1" data-author-popup="auth-Jianfeng-Gao-Aff1" data-author-search="Gao, Jianfeng">Jianfeng Gao</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Angela-Crabtree-Aff3" data-author-popup="auth-Angela-Crabtree-Aff3" data-author-search="Crabtree, Angela">Angela Crabtree</a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0001-6584-8158"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0001-6584-8158</a></span><sup class="u-js-hide"><a href="#Aff3">3</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Jacob-Abel-Aff2" data-author-popup="auth-Jacob-Abel-Aff2" data-author-search="Abel, Jacob">Jacob Abel</a><sup class="u-js-hide"><a href="#Aff2">2</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Christine-Moung_Wen-Aff2" data-author-popup="auth-Christine-Moung_Wen-Aff2" data-author-search="Moung-Wen, Christine">Christine Moung-Wen</a><sup class="u-js-hide"><a href="#Aff2">2</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Brian-Piening-Aff2-Aff3" data-author-popup="auth-Brian-Piening-Aff2-Aff3" data-author-search="Piening, Brian">Brian Piening</a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0002-2683-8157"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0002-2683-8157</a></span><sup class="u-js-hide"><a href="#Aff2">2</a>,<a href="#Aff3">3</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Carlo-Bifulco-Aff2-Aff3" data-author-popup="auth-Carlo-Bifulco-Aff2-Aff3" data-author-search="Bifulco, Carlo">Carlo Bifulco</a><sup class="u-js-hide"><a href="#Aff2">2</a>,<a href="#Aff3">3</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Mu-Wei-Aff1" data-author-popup="auth-Mu-Wei-Aff1" data-author-search="Wei, Mu" data-corresp-id="c1">Mu Wei<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-mail-medium"></use></svg></a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0009-0000-4119-6490"><span class="u-visually-hidden">ORCID: </span>orcid.org/0009-0000-4119-6490</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item c-article-author-list__item--hide-small-screen"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Hoifung-Poon-Aff1" data-author-popup="auth-Hoifung-Poon-Aff1" data-author-search="Poon, Hoifung" data-corresp-id="c2">Hoifung Poon<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-mail-medium"></use></svg></a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0002-9067-0918"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0002-9067-0918</a></span><sup class="u-js-hide"><a href="#Aff1">1</a></sup> & </li><li class="c-article-author-list__show-more" aria-label="Show all 16 authors for this article" title="Show all 16 authors for this article">…</li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Sheng-Wang-Aff4-Aff5" data-author-popup="auth-Sheng-Wang-Aff4-Aff5" data-author-search="Wang, Sheng" data-corresp-id="c3">Sheng Wang<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-mail-medium"></use></svg></a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0002-0439-5199"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0002-0439-5199</a></span><sup class="u-js-hide"><a href="#Aff4">4</a>,<a href="#Aff5">5</a></sup> </li></ul><button aria-expanded="false" class="c-article-author-list__button"><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-down-medium"></use></svg><span>Show authors</span></button> <p class="c-article-info-details" data-container-section="info"> <a data-test="journal-link" href="/nmeth" data-track="click" data-track-action="journal homepage" data-track-category="article body" data-track-label="link"><i data-test="journal-title">Nature Methods</i></a> (<span data-test="article-publication-year">2024</span>)<a href="#citeas" class="c-article-info-details__cite-as u-hide-print" data-track="click" data-track-action="cite this article" data-track-label="link">Cite this article</a> </p> <div class="c-article-metrics-bar__wrapper u-clear-both"> <ul class="c-article-metrics-bar u-list-reset"> <li class=" c-article-metrics-bar__item" data-test="access-count"> <p class="c-article-metrics-bar__count">5970 <span class="c-article-metrics-bar__label">Accesses</span></p> </li> <li class="c-article-metrics-bar__item" data-test="citation-count"> <p class="c-article-metrics-bar__count">1 <span class="c-article-metrics-bar__label">Citations</span></p> </li> <li class="c-article-metrics-bar__item" data-test="altmetric-score"> <p class="c-article-metrics-bar__count">112 <span class="c-article-metrics-bar__label">Altmetric</span></p> </li> <li class="c-article-metrics-bar__item"> <p class="c-article-metrics-bar__details"><a href="/articles/s41592-024-02499-w/metrics" data-track="click" data-track-action="view metrics" data-track-label="link" rel="nofollow">Metrics <span class="u-visually-hidden">details</span></a></p> </li> </ul> </div> </header> <div class="u-js-hide" data-component="article-subject-links"> <h3 class="c-article__sub-heading">Subjects</h3> <ul class="c-article-subject-list"> <li class="c-article-subject-list__subject"><a href="/subjects/image-processing" data-track="click" data-track-action="view subject" data-track-label="link">Image processing</a></li><li class="c-article-subject-list__subject"><a href="/subjects/machine-learning" data-track="click" data-track-action="view subject" data-track-label="link">Machine learning</a></li> </ul> </div> </div> <div class="c-article-body"> <section aria-labelledby="Abs1" data-title="Abstract" lang="en"><div class="c-article-section" id="Abs1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Abs1">Abstract</h2><div class="c-article-section__content" id="Abs1-content"><p>Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. In summary, BiomedParse is an all-in-one tool for biomedical image analysis on all major image modalities, paving the path for efficient and accurate image-based biomedical discovery.</p></div></div></section> <noscript> <div class="c-nature-box c-nature-box--side " data-component="entitlement-box"> <div class="js-access-button"> <a href="https://wayf.springernature.com?redirect_uri=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41592-024-02499-w" class="c-article__button" data-test="ra21"> <svg class="u-icon" width="18" height="18" aria-hidden="true" focusable="false"><use href="#icon-institution"></use></svg> <span class="c-article__button-text">Access through your institution</span> </a> </div> <div class="js-buy-button"> <a href="#access-options" class="c-article__button c-article__button--inverted" data-test="ra21"> <span>Buy or subscribe</span> </a> </div> </div> </noscript> <div class="js-context-bar-sticky-point-mobile" data-track-context="article body"> </div> <div 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class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-1"><figure><figcaption><b id="Fig1" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 1: Overview of BiomedParse and BiomedParseData<i>.</i></b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig1_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig1_HTML.png" alt="" loading="lazy" width="261" height="312"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-2"><figure><figcaption><b id="Fig2" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 2: Comparison on large-scale biomedical image segmentation datasets.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig2_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig2_HTML.png" alt="" loading="lazy" width="258" height="312"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-3"><figure><figcaption><b id="Fig3" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 3: Evaluation on detecting irregular-shaped objects.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig3_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig3_HTML.png" alt="" loading="lazy" width="225" height="312"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-4"><figure><figcaption><b id="Fig4" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 4: Evaluation on object recognition.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig4_HTML.png?as=webp"><img src="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig4_HTML.png" alt="" loading="lazy" width="268" height="312"></picture></div></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-5"><figure><figcaption><b id="Fig5" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 5: Evaluation of BiomedParse on real-world cell segmentation examples.</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><picture><source type="image/webp" srcset="//media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig5_HTML.png?as=webp"><img 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class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Article</span> <span class="c-article-meta-recommendations__access-type">Open access</span> <span class="c-article-meta-recommendations__date">25 November 2024</span> </div> </div> </article> </div> </div> </section> <script> window.dataLayer = window.dataLayer || []; window.dataLayer.push({ recommendations: { recommender: 'semantic', model: 'specter', policy_id: 'NA', timestamp: 1732699910, embedded_user: 'null' } }); </script> <div> <section data-title="Data availability"><div class="c-article-section" id="data-availability-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="data-availability">Data availability</h2><div class="c-article-section__content" id="data-availability-content"> <p>BiomedParseData can be accessed at <a href="https://aka.ms/biomedparse-release">https://aka.ms/biomedparse-release</a>. The three real-world pathology images, including the annotations by pathologists and BiomedParse, can be accessed at <a href="https://aka.ms/biomedparse-release">https://aka.ms/biomedparse-release</a>.</p> </div></div></section><section data-title="Code availability"><div class="c-article-section" id="code-availability-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="code-availability">Code availability</h2><div class="c-article-section__content" id="code-availability-content"> <p>BiomedParse can be accessed at <a href="https://aka.ms/biomedparse-release">https://aka.ms/biomedparse-release</a>, including the model weights and relevant source code. 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IEEE Conference on Computer Vision and Pattern Recognition</i> 580–587 (2014).</p></li></ol><p class="c-article-references__download u-hide-print"><a data-track="click" data-track-action="download citation references" data-track-label="link" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1038/s41592-024-02499-w?format=refman&flavour=references">Download references<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use></svg></a></p></div></div></div></section></div><section data-title="Acknowledgements"><div class="c-article-section" id="Ack1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Ack1">Acknowledgements</h2><div class="c-article-section__content" id="Ack1-content"><p>The authors thank the Microsoft Health and Life Sciences Research team and the Microsoft Health Futures team for support and helpful discussions.</p></div></div></section><section aria-labelledby="author-information" data-title="Author information"><div class="c-article-section" id="author-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="author-information">Author information</h2><div class="c-article-section__content" id="author-information-content"><span class="c-article-author-information__subtitle u-visually-hidden" id="author-notes">Author notes</span><ol class="c-article-author-information__list"><li class="c-article-author-information__item" id="na1"><p>These authors contributed equally: Theodore Zhao, Yu Gu.</p></li></ol><h3 class="c-article__sub-heading" id="affiliations">Authors and Affiliations</h3><ol class="c-article-author-affiliation__list"><li id="Aff1"><p class="c-article-author-affiliation__address">Microsoft Research, Redmond, WA, USA</p><p class="c-article-author-affiliation__authors-list">Theodore Zhao, Yu Gu, Jianwei Yang, Naoto Usuyama, Ho Hin Lee, Sid Kiblawi, Tristan Naumann, Jianfeng Gao, Mu Wei & Hoifung Poon</p></li><li id="Aff2"><p class="c-article-author-affiliation__address">Providence Genomics, Portland, OR, USA</p><p class="c-article-author-affiliation__authors-list">Jacob Abel, Christine Moung-Wen, Brian Piening & Carlo Bifulco</p></li><li id="Aff3"><p class="c-article-author-affiliation__address">Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA</p><p class="c-article-author-affiliation__authors-list">Angela Crabtree, Brian Piening & Carlo Bifulco</p></li><li id="Aff4"><p class="c-article-author-affiliation__address">Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA</p><p class="c-article-author-affiliation__authors-list">Sheng Wang</p></li><li id="Aff5"><p class="c-article-author-affiliation__address">Department of Surgery, University of Washington, Seattle, WA, USA</p><p class="c-article-author-affiliation__authors-list">Sheng Wang</p></li></ol><div class="u-js-hide u-hide-print" data-test="author-info"><span class="c-article__sub-heading">Authors</span><ol class="c-article-authors-search u-list-reset"><li id="auth-Theodore-Zhao-Aff1"><span class="c-article-authors-search__title u-h3 js-search-name">Theodore Zhao</span><div class="c-article-authors-search__list"><div class="c-article-authors-search__item c-article-authors-search__list-item--left"><a href="/search?author=Theodore%20Zhao" class="c-article-button" data-track="click" data-track-action="author link - publication" data-track-label="link" rel="nofollow">View author publications</a></div><div class="c-article-authors-search__item c-article-authors-search__list-item--right"><p class="search-in-title-js c-article-authors-search__text">You can also search for this author in <span class="c-article-identifiers"><a class="c-article-identifiers__item" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&term=Theodore%20Zhao" data-track="click" data-track-action="author link - 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pubmed" data-track-label="link" rel="nofollow">PubMed</a><span class="u-hide"> </span><a class="c-article-identifiers__item" href="http://scholar.google.co.uk/scholar?as_q=&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_sauthors=%22Sheng%20Wang%22&as_publication=&as_ylo=&as_yhi=&as_allsubj=all&hl=en" data-track="click" data-track-action="author link - scholar" data-track-label="link" rel="nofollow">Google Scholar</a></span></p></div></div></li></ol></div><h3 class="c-article__sub-heading" id="contributions">Contributions</h3><p>T.Z., Y.G., J.Y., N.U., M.W., H.P. and S.W. contributed to the conception and design of the work. T.Z. contributed to the data acquisition and curation of BiomedParseData. T.Z., J.Y., N.U. and M.W. contributed to BiomedParse model training. Y.G., T.Z., H.L., N.U. and S.K. contributed to the evaluation of BiomedParse and baseline models. T.N. and J.G. contributed to the technical discussions. A.C., J.A., C.M., B.P. and C.B. provided clinical inputs to the study. All authors contributed to the drafting and revision of the manuscript.</p><h3 class="c-article__sub-heading" id="corresponding-author">Corresponding authors</h3><p id="corresponding-author-list">Correspondence to <a id="corresp-c1" href="mailto:muhsin.wei@microsoft.com">Mu Wei</a>, <a id="corresp-c2" href="mailto:hoifung@microsoft.com">Hoifung Poon</a> or <a id="corresp-c3" href="mailto:swang@cs.washington.edu">Sheng Wang</a>.</p></div></div></section><section data-title="Ethics declarations"><div class="c-article-section" id="ethics-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="ethics">Ethics declarations</h2><div class="c-article-section__content" id="ethics-content"> <h3 class="c-article__sub-heading" id="FPar4">Competing interests</h3> <p>C.B. is a member of the scientific advisory board and owns stock in PrimeVax and BioAI; is on the scientific board of Lunaphore and SironaDx; has a consultant or advisory relationship with Sanofi, Agilent, Roche and Incendia; contributes to institutional research for Illumina, and is an inventor on US patent applications US20180322632A1 (Image Processing Systems and Methods for Displaying Multiple Images of a Biological Specimen) filed by Ventana Medical Systems, Providence Health and Services Oregon and US20200388033A1 (System and Method for Automatic Labeling of Pathology Images) filed by Providence Health and Services Oregon, Omics Data Automation. The other authors declare no competing interests.</p> </div></div></section><section data-title="Peer review"><div class="c-article-section" id="peer-review-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="peer-review">Peer review</h2><div class="c-article-section__content" id="peer-review-content"> <h3 class="c-article__sub-heading" id="FPar3">Peer review information</h3> <p><i>Nature Methods</i> thanks Stefania Moroianu, Dong Ni, Yichi Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the <i>Nature Methods</i> team. <a data-track="click" data-track-label="link" data-track-action="supplementary material anchor" href="/articles/s41592-024-02499-w#MOESM3">Peer reviewer reports</a> are available.</p> </div></div></section><section data-title="Additional information"><div class="c-article-section" id="additional-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="additional-information">Additional information</h2><div class="c-article-section__content" id="additional-information-content"><p><b>Publisher’s note</b> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></div></div></section><section data-title="Extended data"><div class="c-article-section" id="Sec21-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec21">Extended data</h2><div class="c-article-section__content" id="Sec21-content"><div data-test="supplementary-info"><div id="figshareContainer" class="c-article-figshare-container" data-test="figshare-container"></div><div class="c-article-supplementary__item js-c-reading-companion-figures-item" data-test="supp-item" id="Fig6"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="extended data fig. 1 number of images in each of t" href="/articles/s41592-024-02499-w/figures/6" data-supp-info-image="//media.springernature.com/lw685/springer-static/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig6_ESM.jpg">Extended Data Fig. 1 Number of images in each of the 25 anatomic sites from 9 modalities.</a></h3><div class="c-article-supplementary__description" data-component="thumbnail-container"><p>One anatomic site could present in multiple modalities.</p></div></div><div class="c-article-supplementary__item js-c-reading-companion-figures-item" data-test="supp-item" id="Fig7"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="extended data fig. 2 ablation studies comparing th" href="/articles/s41592-024-02499-w/figures/7" data-supp-info-image="//media.springernature.com/lw685/springer-static/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig7_ESM.jpg">Extended Data Fig. 2 Ablation studies comparing the performance of BiomedParse and two variants.</a></h3><div class="c-article-supplementary__description" data-component="thumbnail-container"><p>BiomedParse-SAM stands for using SAM to initialize the image encoder. BiomedParse-PubmedBERT stands for using the frozen PubmedBERT as the text encoder. Each modality category contains multiple object types. Each object type was aggregated as the instance median to be shown in the plot. N in the plot denotes the number of images in the corresponding modality. The numbers of object types in each modality are as follows: N = 112 for All, N = 27 for CT, N = 34 for MRI, N = 12 for X-Ray, N = 24 for Pathology, N = 7 for Ultrasound, N = 2 for Fundus, N = 3 for Endoscope, N = 2 for Dermoscopy, and N = 1 for OCT. Each box shows the quartiles of the distribution, with the center as the median, the minimum as the first quartile, and the maximum as the third quartile. The whiskers extend to the farthest data point that lies within 2 times the inter-quartile range (IQR) from the nearest quartile. Data points that lie outside the whiskers are shown as fliers. *indicates the significance level at which BiomedParse outperforms BiomedParse-PubmedBERT, with two-sided paired t-test p-value < 1 × 10<sup>-2</sup> for **, p-value < 1 × 10<sup>-3</sup> for ***, p-value < 1 × 10<sup>-4</sup> for ****. Exact p-values for the comparison between BiomedParse and BiomedParse-PubMedBERT are as follows: p-value < 9.52 × 10<sup>-10</sup> for All, p-value < 1.67 × 10<sup>-3</sup> for CT, p-value < 4.87 × 10<sup>-4</sup> for MRI, p-value < 1.98 × 10<sup>-4</sup> for Pathology, and p-value < 7.13 × 10<sup>-3</sup> for Ultrasound.</p></div></div><div class="c-article-supplementary__item js-c-reading-companion-figures-item" data-test="supp-item" id="Fig8"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="extended data fig. 3 evaluating biomedparse and co" href="/articles/s41592-024-02499-w/figures/8" data-supp-info-image="//media.springernature.com/lw685/springer-static/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig8_ESM.jpg">Extended Data Fig. 3 Evaluating BiomedParse and competing methods in terms of Average Symmetric Surface Distance.</a></h3><div class="c-article-supplementary__description" data-component="thumbnail-container"><p>Box plot comparing the performance of BiomedParse and competing methods in terms of Average Symmetric Surface Distance (ASSD). Smaller ASSD indicates better segmentation performance. Each box shows the quartiles of the distribution, with center as the median, minimum as the first quartile, and maximum as the third quartile. The whiskers extend to the farthest data point that lies within 2 times the inter-quartile range (IQR) from the nearest quartile. Data points that lie outside the whiskers are shown as fliers. Each modality category contains multiple object types. Each object type was aggregated as the instance median to be shown in the plot. The numbers of object types in each modality are as follows: n = 112 for All, n = 27 for CT, n = 34 for MRI, n = 12 for X-Ray, n = 24 for Pathology, n = 7 for Ultrasound, n = 2 for Fundus, n = 3 for Endoscope, n = 2 for Dermoscopy, and n = 1 for OCT. *indicates the significance level at which BiomedParse outperforms the best-competing method, with two-sided paired t-test p-value < 1 × 10<sup>-2</sup> for **, p-value < 1 × 10<sup>-3</sup> for ***, p-value < 1 × 10<sup>-4</sup> for ****. Exact p-values for the comparison between BiomedParse and MedSAM with oracle box prompt are as follows: p-value < 3.43 × 10<sup>-6</sup> for All, p-value < 2.61 × 10<sup>-3</sup> for CT, p-value < 7.73 × 10<sup>-5</sup> for MRI, and p-value < 2.94 × 10<sup>-8</sup> for Pathology.</p></div></div><div class="c-article-supplementary__item js-c-reading-companion-figures-item" data-test="supp-item" id="Fig9"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="extended data fig. 4 comparing biomedparse with bi" href="/articles/s41592-024-02499-w/figures/9" data-supp-info-image="//media.springernature.com/lw685/springer-static/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig9_ESM.jpg">Extended Data Fig. 4 Comparing BiomedParse with biomedical-specific text prompt segmentation models.</a></h3><div class="c-article-supplementary__description" data-component="thumbnail-container"><p>Bar plot comparing BiomedParse with biomedical-specific text prompt segmentation models across different organs on CT in terms of Dice score. Each bar shows the mean of the distribution, with error bar indicating the 95% confidence interval. The sample sizes for the target organs are as follows: n = 27,779 for All, n = 4,409 for Aorta, n = 864 for Bladder, n = 1,677 for Duodenum, n = 1,964 for Esophagus, n = 712 for Gallbladder, n = 4,105 for Inferior vena cava, n = 635 for Left adrenal gland, n = 1,776 for Left kidney, n = 4,648 for Liver, n = 1,345 for Pancreas, n = 571 for Right adrenal gland, n = 1,649 for Right kidney, n = 1,587 for Spleen, and n = 1,837 for Stomach. *indicates the significance level at which BiomedParse outperforms the best-competing method, with two-sided paired t-test p-value < 1 × 10<sup>-2</sup> for **, p-value < 1 × 10<sup>-3</sup> for ***, p-value < 1 × 10<sup>-4</sup> for ****. Exact p-values for the comparison between BiomedParse and SegVol are as follows: p-value < 2.23 × 10<sup>-308</sup> for All, p-value < 1.86 × 10<sup>-58</sup> for Aorta, p-value < 1.73 × 10<sup>-7</sup> for Bladder, p-value < 3.44 × 10<sup>-86</sup> for Duodenum, p-value < 5.00 × 10<sup>-185</sup> for Esophagus, p-value < 3.37 × 10<sup>-15</sup> for Gallbladder, p-value < 6.28 × 10<sup>-99</sup> for Inferior vena cava, p-value < 5.08 × 10<sup>-10</sup> for Left adrenal gland, p-value < 9.26 × 10<sup>-31</sup> for Left kidney, p-value < 3.31 × 10<sup>-37</sup> for Liver, p-value < 2.27 × 10<sup>-56</sup> for Pancreas, p-value < 1.01 × 10<sup>-16</sup> for Right adrenal gland, p-value < 2.98 × 10<sup>-20</sup> for Right kidney, p-value < 1.09 × 10<sup>-20</sup> for Spleen, and p-value < 4.68 × 10<sup>-25</sup> for Stomach.</p></div></div><div class="c-article-supplementary__item js-c-reading-companion-figures-item" data-test="supp-item" id="Fig10"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="extended data fig. 5 comparing biomedparse with fi" href="/articles/s41592-024-02499-w/figures/10" data-supp-info-image="//media.springernature.com/lw685/springer-static/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig10_ESM.jpg">Extended Data Fig. 5 Comparing BiomedParse with fine-tuned SAM and MedSAM.</a></h3><div class="c-article-supplementary__description" data-component="thumbnail-container"><p>Bar plot comparing BiomedParse and SAM and MedSAM when SAM and MedSAM are both further trained on the entire BiomedParseData. Both SAM and MedSAM were provided with oracle bounding box around the segmentation target during the training and the inference stage. Each bar shows the mean of the distribution, with error bar indicating the 95% confidence interval. Each modality category contains multiple object types. Each object type was aggregated as the instance median to be shown in the plot. We show the numbers of object types in each modality are as follows. The numbers of object types in each modality are as follows: n = 105 for All, n = 26 for CT, n = 34 for MRI, n = 6 for X-Ray, n = 24 for Pathology, n = 7 for Ultrasound, n = 2 for Fundus, n = 3 for Endoscope, n = 2 for Dermoscopy, and n = 1 for OCT. *indicates the significance level at which BiomedParse outperforms the best-competing method, with two-sided paired t-test p-value < 1 × 10<sup>-2</sup> for **, p-value < 1 × 10<sup>-3</sup> for ***, p-value < 1 × 10<sup>-4</sup> for ****. Exact p-values for the comparison between BiomedParse and SAM-FT with oracle box prompt are as follows: p-value < 1.78 × 10<sup>-7</sup> for All, p-value < 2.02 × 10<sup>-2</sup> for CT, p-value < 1.32 × 10<sup>-2</sup> for X-Ray, p-value < 3.52 × 10<sup>-8</sup> for Pathology, and p-value < 1.49 × 10<sup>-2</sup> for Ultrasound.</p></div></div><div class="c-article-supplementary__item js-c-reading-companion-figures-item" data-test="supp-item" id="Fig11"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="extended data fig. 6 comparison between biomedpars" href="/articles/s41592-024-02499-w/figures/11" data-supp-info-image="//media.springernature.com/lw685/springer-static/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig11_ESM.jpg">Extended Data Fig. 6 Comparison between BiomedParse and competing methods on the MedSAM benchmark.</a></h3><div class="c-article-supplementary__description" data-component="thumbnail-container"><p>We evaluated MedSAM and SAM using the ground truth bounding box for the segmentation.For nnU-Net and DeepLabV3+, we reported the evaluation reported by MedSAM. Results are shown by imaging modality, with statistical significance comparison between BiomedParse and best-competing method MedSAM. Each box shows the quartiles of the distribution, with center as the median, minimum as the first quartile, and maximum as the third quartile. The whiskers extend to the farthest data point that lies within 2 times the inter-quartile range (IQR) from the nearest quartile. Data points that lie outside the whiskers are shown as fliers. Each modality category contains multiple object types. Each object type was aggregated as the instance median to be shown in the plot. The numbers of object types in each modality are as follows: n = 50 for All, n = 18 for CT, n = 15 for MRI, n = 6 for X-Ray, n = 1 for Pathology, n = 6 for Ultrasound, n = 2 for Fundus, n = 1 for Endoscope, and n = 1 for Dermoscopy. * indicates the significance level at which BiomedParse outperforms the best-competing method, with two-sided paired t-test p-value < 1 × 10<sup>-2</sup> for **, p-value < 1 × 10<sup>-3</sup> for ***, p-value < 1 × 10<sup>-4</sup> for ****. Exact p-values for the comparison between BiomedParse and MedSAM with oracle box prompt are as follows: p-value < 2.98 × 10<sup>-3</sup> for All, p-value < 7.08 × 10<sup>-3</sup> for CT, and p-value < 4.35 × 10<sup>-2</sup> for MRI.</p></div></div><div class="c-article-supplementary__item js-c-reading-companion-figures-item" data-test="supp-item" id="Fig12"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="extended data fig. 7 comparing the improvement of " href="/articles/s41592-024-02499-w/figures/12" data-supp-info-image="//media.springernature.com/lw685/springer-static/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_Fig12_ESM.jpg">Extended Data Fig. 7 Comparing the improvement of BiomedParse over SAM with shape irregularity.</a></h3><div class="c-article-supplementary__description" data-component="thumbnail-container"><p>Scatter plots comparing the improvement of BiomedParse over SAM with shape irregularity in terms of box ratio (left), convex ratio (middle), and inversed rotational inertia (right). Each dot represents the mean statistics over one object type in our segmentation ontology. We show the regression plot with the 95 confidence interval as the error bands. The p-values show the two-sided Wald test results.</p></div></div></div></div></div></section><section data-title="Supplementary information"><div class="c-article-section" id="Sec22-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec22">Supplementary information</h2><div class="c-article-section__content" id="Sec22-content"><div data-test="supplementary-info"><div class="c-article-supplementary__item" data-test="supp-item" id="MOESM1"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="supplementary information" href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_MOESM1_ESM.pdf" data-supp-info-image="">Supplementary Information</a></h3><div class="c-article-supplementary__description" data-component="thumbnail-container"><p>Supplementary Table 1, Figs. 1–11 and References.</p></div></div><div class="c-article-supplementary__item" data-test="supp-item" id="MOESM2"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="reporting summary" href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_MOESM2_ESM.pdf" data-supp-info-image="">Reporting Summary</a></h3></div><div class="c-article-supplementary__item" data-test="supp-item" id="MOESM3"><h3 class="c-article-supplementary__title u-h3"><a class="print-link" data-track="click" data-track-action="view supplementary info" data-test="supp-info-link" data-track-label="peer review file" href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41592-024-02499-w/MediaObjects/41592_2024_2499_MOESM3_ESM.pdf" data-supp-info-image="">Peer Review File</a></h3></div></div></div></div></section><section data-title="Rights and permissions"><div class="c-article-section" id="rightslink-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="rightslink">Rights and permissions</h2><div class="c-article-section__content" id="rightslink-content"><p>Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p><p class="c-article-rights"><a data-track="click" data-track-action="view rights and permissions" data-track-label="link" href="https://s100.copyright.com/AppDispatchServlet?title=A%20foundation%20model%20for%20joint%20segmentation%2C%20detection%20and%20recognition%20of%20biomedical%20objects%20across%20nine%20modalities&author=Theodore%20Zhao%20et%20al&contentID=10.1038%2Fs41592-024-02499-w&copyright=The%20Author%28s%29%2C%20under%20exclusive%20licence%20to%20Springer%20Nature%20America%2C%20Inc.&publication=1548-7091&publicationDate=2024-11-18&publisherName=SpringerNature&orderBeanReset=true">Reprints and permissions</a></p></div></div></section><section aria-labelledby="article-info" data-title="About this article"><div class="c-article-section" id="article-info-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="article-info">About this article</h2><div class="c-article-section__content" id="article-info-content"><div class="c-bibliographic-information"><div class="u-hide-print c-bibliographic-information__column c-bibliographic-information__column--border"><a data-crossmark="10.1038/s41592-024-02499-w" target="_blank" rel="noopener" href="https://crossmark.crossref.org/dialog/?doi=10.1038/s41592-024-02499-w" data-track="click" data-track-action="Click Crossmark" data-track-label="link" data-test="crossmark"><img loading="lazy" width="57" height="81" alt="Check for updates. 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A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities | Nature Methods